Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Top 10 algorithms in data mining
Knowledge and Information Systems
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An empirical study of applying ensembles of heterogeneous classifiers on imperfect data
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Relationship between diversity and correlation in multi-classifier systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Ensemble based sensing anomaly detection in wireless sensor networks
Expert Systems with Applications: An International Journal
Improving bagging performance through multi-algorithm ensembles
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Heterogeneous ensemble for feature drifts in data streams
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
| Hi-index | 0.00 |
In this paper, we introduce the use of combinations of heterogeneous classifiers to achieve better diversity. Conducting theoretical and empirical analyses of the diversity of combinations of heterogeneous classifiers, we study the relationship between heterogeneity and diversity. On the one hand, the theoretical analysis serves as a foundation for employing heterogeneous classifiers in Multi-Classifier Systems or ensembles. On the other hand, experimental results provide empirical evidence. We consider synthetic as well as real data sets, utilize classification algorithms that are essentially different, and employ various popular diversity measures for evaluation. Two interesting observations will contribute to the future design of Multi-Classifier Systems and ensemble techniques. First, the diversity among heterogeneous classifiers is higher than that among homogeneous ones, and hence using heterogeneous classifiers to construct classifier combinations would increase the diversity. Second, the heterogeneity primarily results from different classification algorithms rather than the same algorithm with different parameters.